The key step of semi-supervised methods is to infer accurate pixel-level labels for a large number of images with only image-level weak annotations. One popular choice is Class Activation Map (CAM) [1]}, which highlights local discriminative regions by investigating the contribution of hidden units from a deep classification model. However, the generated object regions are small or sparse, and not sufficient to cover entire object area. In order to generate dense object regions, most previous methods regard these initial discriminative regions with high confidence as segmentation seeds. Then they focus on expanding them with different techniques like learning pixel-level affinities [2]}, adversarial erasing manner [3]} or stochastic regularization [4]}. As shown in Figure REF (d), we can see that the regions with high confidence do not mean that their corresponding labels are always correct. We experiment on the PASCAL VOC 2012 trainaug set and the accuracy of seed regions is only 86.7% [5]}, which largely affect the following expansion process.
